Our mission at Glassbeam is to equip our customers with the ability to predict equipment failures. Prevention is better than cure after all. And there’s that added incentive of saving dollars by proactive maintenance rather than adhoc, reactive ways.
The ground reality, however, is that not all incidents can be prevented. So what does a field service engineer do if they need to immediately react to a high priority incident?
We are now in an era where machine learning isn't just hype. In fact, it is absolutely real in its business impact as Glassbeam has recently demonstrated for its growing network of connected medical imaging equipment.
With Glassbeam's extensive experience in data engineering and analytics related to GE CT Scanner machine logs, we now have dozens of powerful use cases where automated anomaly detection via machine learning has been used to detect potentially expensive part failures well before the end-user even noticed an issue. Here are 3 real-world examples:
Having met several C-suite executives in recent customer meetings, it has become increasingly clear to me the immense value and ROI we bring to the table for healthcare providers using our AI/ML analytics platform.
In Part 1 of this blog series, I set the stage on the debate on who owns the machine data generated by medical devices such as CT, MRI, and so on. In Part 2 of this blog, I outlined an approach and my perspective on how this debate is being resolved between Providers and OEMs.
For the first time in the Healthcare industry, two distinctly different groups in a healthcare provider organization come under the eye of a single pane of glass. The roles are Clinical Engineering practitioners responsible for machine uptime and other is the Radiology and Imaging groups responsible for maximizing machine utilization and therefore revenues. The common goal is always improving patient care and clinical outcomes for the benefits of its customers.
Next week, Glassbeam is gearing up to participate at AMMI Exchange 2019 conference in Columbus, Ohio. The event promises fantastic insights into the concerns and questions of Radiology and Clinical Engineering professionals and some great line up of talks from all walks of Healthcare Technology Management (HTM).
In Part 1 of this blog series, I set the stage to understand who owns the machine data generated by medical devices such as CT, MRI, and so on. We also discussed how the restrictions on device data evolved over time and the implications on healthcare providers’ maintenance programs.